Active and passive sensing technologies are providing powerful mechanisms to track, model, and understand a range of health behaviors and well-being states. Despite yielding rich, dense and high fidelity data, current sensing technologies often require highly engineered study designs and persistent participant compliance, making them difficult to scale to large populations and to data acquisition tasks spanning extended time periods. This paper situates social media as a new passive, unobtrusive sensing technology. We propose a semi-supervised machine learning framework to combine small samples of data gathered through active sensing, with large-scale social media data to infer mood instability (MI) in individuals. Starting from a theoretically-grounded measure of MI obtained from mobile ecological momentary assessments (EMAs), we show that our model is able to infer MI in a large population of Twitter users with 96% accuracy and F-1 score. Additionally, we show that, our model predicts self-identifying Twitter users with bipolar and borderline personality disorder to exhibit twice the likelihood of high MI, compared to that in a suitable control. We discuss the implications and the potential for integrating complementary sensing capabilities to address complex research challenges in precision medicine.

In this paper, we use mixed methods to study a controversial Internet site: The Kotaku in Action (KiA) subreddit. Members of KiA are part of GamerGate, a distributed social movement. We present an emic account of what takes place on KiA: who are they, what are their goals and beliefs, and what rules do they follow. Members of GamerGate in general and KiA in particular have often been accused of harassment. However, KiA site policies explicitly prohibit such behavior, and members insist that they have been falsely accused. Underlying the controversy over whether KiA supports harassment is a complex disagreement about what “harassment” is, and where to draw the line between freedom of expression and censorship. We propose a model that characterizes perceptions of controversial speech, dividing it into four categories: criticism, insult, public shaming, and harassment. We also discuss design solutions that address the challenges of moderating harassment without impinging on free speech, and communicating across different ideologies.

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Ecological Momentary Assessment (EMA) methods have emerged as an approach that enhances the ecological validity of data collected for the study of human behavior and experience. In particular, EMA methods are used to capture individuals' experiences (e.g., symptoms, affect, and behaviors) in real-world contexts and in near-real time. However, work investigating participants' experiences in EMA studies and in particular, how these experiences may influence the collected data, is limited. We conducted in-depth focus groups with 32 participants following an EMA study on mental well-being in college students. In doing so, we probed how the elicitation of high-quality, reflective responses is related to the design of EMA interactions. Through our study, we distilled three primary considerations for designing EMA interactions, based on observations of 1) response strategies to repeated questions, 2) the perceived burden of EMA prompts, and 3) challenges to the validity and robustness of EMA data. We present these considerations in the context of two microinteraction-based EMA approaches that we tested: lock-screen EMA and image-based question prompts. We conclude by characterizing design tensions in the presentation and delivery of EMA prompts, and outline directions for future work to address these tensions.

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